Lexicon Optimization for Automatic Speech Recognition based on Discriminative Learning
نویسندگان
چکیده
In agglutinative languages such as Japanese and Uyghur, selection of lexical unit is not obvious and one of the important issues in designing language model for automatic speech recognition (ASR). In this paper, we propose a discriminative learning method to select word entries which would reduce the word error rate (WER). We define an evaluation function for each word by a set of features and their weights, and the measure for optimization by the difference of WERs by the two units (morpheme and word). Then, the weights of the features are learned by a perceptron algorithm. Finally, word entries with higher evaluation scores are selected. The discriminative method is successfully applied to an Uyghur large-vocabulary continuous speech recognition system, resulting in a significant reduction of WER without a drastic increase of the vocabulary size.
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